Abstract
In the field of dental biometrics, textural information plays a significant role very often in tissue characterization and gum diseases diagnosis, in addition to morphology and intensity. Failure to diagnose gum diseases in its early stages may leads to oral cancer. Dental biometrics has emerged as vital biometric information of human being due to its stability, invariant nature and uniqueness. The objective of this paper is to improve the classification accuracy based on fused LBP and SIFT textural features for the development of a computer assisted screening system. The swift expansion of dental images has enforced the requirement of efficient dental image retrieval system for retrieving images that are visually similar to query image. This paper implements a dental image retrieval system using fused LBP & SIFT features. The fused LBP & SIFT features identify the gum diseases from the epithelial layer in classifying normal dental images about 91.6% more accurately compared to other features.
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References
Sable, D.R.G.: Teeth Feature Extraction and Matching for Human Identification Using Scale Invariant Feature Transform Algorithm. European Journal of Advances in Engineering and Technology (2015)
Velmurugan, K.: A Survey of Content-Based Image Retrieval Systems using Scale-Invariant Feature Transform (SIFT). International Journal of Advanced Re-search in Computer Science and Software Engineering 4, January 2014
Tayade, Y.R., Bansode, S.M.: An Efficient Face Recognition and Retrieval Using LBP and SIFT. International Journal of Advanced Research in Computer and Communication Engineering 2, April 2013
Senthil Kumar, R., Senthilmurugan, M.: Content-Based Image Retrieval System in Medical Applications. International Journal of Engineering Research & Technology 3, March 2013
Ren, J., Jiang, X., Yuan, J.: Dynamic texture recognition using en-hanced LBP features. In: IEEE Int. Conf. Acoustics, Speech, and Signal Processing, May 2013
Malik, F., Baharudin, B.: Analysis of distance metrics in content-based im-age retrieval using statistical quantized histogram texture features in the DCT do-main. Journal of King Saud University-Computer and Information Sciences 25, 207–218 (2013)
Hussain, S.A., Holambe, A.N., Shaikh, Z.: A Comparative Study Of Diffrent Transformation Techniques For Cbir. International Journal of Engineering Research and Technology 1, October 2012
Oberoi, A., Singh, M.: Content Based Image Retrieval System for Medi-cal Databases (CBIR-MD) - Lucratively tested on Endoscopy, Dental and Skull Images. International Journal of Computer Science 9, May 2012
Oberoi, A., Sharma, D., Singh, M.: CBIR-MD/BGP: CBIR-MD System based on Bipartite Graph Partitioning. International Journal of Computer Applications 52, August 2012
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Suganya, R., Rajaram, S., Vishalini, S., Meena, R., Senthil Kumar, T. (2016). Dental Image Retrieval Using Fused Local Binary Pattern & Scale Invariant Feature Transform. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, KC., Mosin, S., Ma, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-319-28658-7_18
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DOI: https://doi.org/10.1007/978-3-319-28658-7_18
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